{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9680cfe3-103e-4232-b6e5-679e7c0b417d",
   "metadata": {},
   "source": [
    "- **数据导入与简单绘图**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a290fd15-dd39-47d0-9fd6-d01ecde88ed0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1202, 3)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r\"C:\\Users\\Shuyu\\Desktop\\single_stock.csv\",index_col=0)\n",
    "data.head()\n",
    "data.shape\n",
    "\n",
    "timeseries = data[[\"Close\"]].values.astype('float32')\n",
    "\n",
    "plt.figure(dpi=300,figsize=(10,3))\n",
    "plt.plot(timeseries)\n",
    "plt.show()\n",
    "# 简单的进行一些可视化,直观的检查一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b7ab58f-eb2f-43e7-b446-5cd83c05fe1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3000x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d9430c91-7bd9-4f50-b0d4-4e9b504f8a06",
   "metadata": {},
   "source": [
    "- **训练集与测试集分割**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "823d234d-4a78-4e40-90f5-ae7be807af1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#不能改变时间顺序，因此没有使用train_test_split等操作\n",
    "train_size = int(len(timeseries) * 0.67)\n",
    "test_size = len(timeseries) - train_size\n",
    "train, test = timeseries[:train_size], timeseries[train_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "33706e51-5be5-4a65-bbec-83b7fa8f2f25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(805, 1)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d87bac27-75d5-4e70-8200-1b7730561ae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(397, 1)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "0d9db772-e080-4721-a763-5eb139f91639",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert data into Tensors\n",
    "train_tensor = torch.FloatTensor(train).view(-1, train.shape[0], 1)\n",
    "test_tensor = torch.FloatTensor(test).view(-1, test.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "542d7dfc-ec88-4ebb-97d6-57e75a019f29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 805, 1])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_tensor.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "5d381c66-2027-4a4a-9587-932232cf02c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 397, 1])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_tensor.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30644d10-775f-437f-8c6a-bd16fc0d0e80",
   "metadata": {},
   "source": [
    "- **定义网络架构**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "c3e6ed80-0661-49c4-a3ff-67fde1c84945",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTMModel(nn.Module):\n",
    "    def __init__(self, input_size=1, hidden_size=50, num_layers=1, output_size=1):\n",
    "        super(LSTMModel, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.num_layers = num_layers\n",
    "\n",
    "        #LSTM层\n",
    "        self.lstm = nn.LSTM(input_size, hidden_size, num_layers,batch_first=True)\n",
    "\n",
    "        #输出层\n",
    "        self.fc = nn.Linear(hidden_size, output_size)\n",
    "\n",
    "    def forward(self, x):\n",
    "        #初始化h0和c0\n",
    "        h0 = torch.rand(self.num_layers, x.size(0), self.hidden_size).requires_grad_()\n",
    "        c0 = torch.rand(self.num_layers, x.size(0), self.hidden_size).requires_grad_()\n",
    "\n",
    "        #LSTM的向前传播，此时我们关注的是所有时间步上的输出，还是最后一个时间步上的输出？\n",
    "        #所有时间步上的输出，因此我们要输出的是output，而不是hn和cn\n",
    "        output, (_, _) = self.lstm(x, (h0.detach(), c0.detach()))\n",
    "\n",
    "        #output的结构为[batch_size, seq_len, hidden_size]\n",
    "        #因此我们要取出对我们有用的维度\n",
    "        out = self.fc(output[:, :, :])\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "0963c9c7-f0b4-4fff-bc1b-ffc47dc456d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 805, 1])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LSTMModel() #实例化\n",
    "model(train_tensor).shape #尝试查看输出数据的结构"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2c69f33-6627-45dc-8763-9d0cc0a5009d",
   "metadata": {},
   "source": [
    "- **损失要多少才算合理？**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "4d680ba9-e995-4fc5-a4b5-566303e83a94",
   "metadata": {},
   "outputs": [],
   "source": [
    "def MSE(Y_ture,Y_predict):\n",
    "    return ((Y_ture - Y_predict)**2).sum()/Y_ture.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ec296ca-34f1-410a-8b5e-4a36f4e0072a",
   "metadata": {},
   "source": [
    "如果不使用神经网络预测，只将均值当做预测标签计算MSE的话——"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "ba540b76-c16e-42ca-9ce3-2c98f0a2bc67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181625.20248447204"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSE(train,train.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "965d2e54-955e-461a-8446-9d8a6b0780e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35148.75818639799"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSE(test,test.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18001eff-9db3-4b6c-9e37-490b53c3058a",
   "metadata": {},
   "source": [
    "- **第一轮超参数设置与架构设置**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "2b3831da-c93f-4851-ae0a-a7eb4ba31036",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [50/300], Loss: 10208146.0000\n",
      "Epoch [100/300], Loss: 10165716.0000\n",
      "Epoch [150/300], Loss: 10129233.0000\n",
      "Epoch [200/300], Loss: 10094029.0000\n",
      "Epoch [250/300], Loss: 10048600.0000\n",
      "Epoch [300/300], Loss: 10000588.0000\n",
      "训练完成\n"
     ]
    }
   ],
   "source": [
    "#超参数设置\n",
    "input_size = 1\n",
    "hidden_size = 50\n",
    "num_layers = 1\n",
    "output_size = 1\n",
    "learning_rate = 0.01\n",
    "num_epochs = 300\n",
    "\n",
    "#实例化模型\n",
    "model1 = LSTMModel(input_size, hidden_size, num_layers, output_size)\n",
    "\n",
    "#定义损失函数与优化算法\n",
    "criterion = nn.MSELoss(reduction=\"mean\")\n",
    "optimizer = torch.optim.Adam(model1.parameters(), lr=learning_rate)\n",
    "\n",
    "#开始进行训练的循环\n",
    "for epoch in range(num_epochs):\n",
    "    outputs = model1(train_tensor)\n",
    "    optimizer.zero_grad()\n",
    "    loss = criterion(outputs, train_tensor[:, :, :])\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    if (epoch+1) % 50 == 0:\n",
    "        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n",
    "\n",
    "print(\"训练完成\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "f2290d28-8aea-4650-9f2a-6245f1baa098",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8168489.914357683"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.eval()\n",
    "test_outputs = model1(test_tensor).detach().numpy()\n",
    "MSE(test,test_outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a037d8d-93f4-4438-a109-358ff48f0885",
   "metadata": {},
   "source": [
    "从上述训练结果中，不难发现以下问题：\n",
    "\n",
    "1. **初始损失非常高**，可能说明模型学习能力或初始化技巧有很大的问题\n",
    "\n",
    "2. **损失下降速度非常缓慢**，说明学习效率和迭代效率有很大问题\n",
    "\n",
    "3. **迭代次数明显不够**，在300个epoch之后损失是直接使用均值进行预测的30多倍……\n",
    "\n",
    "4. **测试集上的损失也非常高**，模型处于完全失效的状态\n",
    "\n",
    "因此，我们可以采取以下的解决方案：\n",
    "\n",
    "1. **增加LSTM的复杂度**来提升学习能力，现在的LSTM架构过于简单，难以满足当前任务的需求。\n",
    "\n",
    "2. 如何提升学习效率和迭代效率？**提升学习率**，同时也可以提升batch_size（如果有batch_size的话）。\n",
    "\n",
    "3. **增加总迭代次数**，给模型更多的训练空间"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09795b12-31d6-4ead-90fb-9d03a367e85c",
   "metadata": {},
   "source": [
    "- **第二轮超参数与架构设置**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "de6594b7-c7b0-450f-a3b0-8a354acfdef2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [150/1500], Loss: 3233892.7500\n",
      "Epoch [300/1500], Loss: 1416913.1250\n",
      "Epoch [450/1500], Loss: 535400.3125\n",
      "Epoch [600/1500], Loss: 159067.1250\n",
      "Epoch [750/1500], Loss: 49066.5977\n",
      "Epoch [900/1500], Loss: 8391.8633\n",
      "Epoch [1050/1500], Loss: 677.6950\n",
      "Epoch [1200/1500], Loss: 293.2071\n",
      "Epoch [1350/1500], Loss: 246.6494\n",
      "Epoch [1500/1500], Loss: 287.8425\n",
      "训练完成\n"
     ]
    }
   ],
   "source": [
    "#超参数设置\n",
    "input_size = 1\n",
    "hidden_size = 100\n",
    "num_layers = 5\n",
    "output_size = 1\n",
    "learning_rate = 0.1\n",
    "num_epochs = 1500\n",
    "\n",
    "#实例化模型\n",
    "model = LSTMModel(input_size, hidden_size, num_layers, output_size)\n",
    "\n",
    "#定义损失函数与优化算法\n",
    "criterion = nn.MSELoss(reduction=\"mean\")\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "#开始进行训练的循环\n",
    "for epoch in range(num_epochs):\n",
    "    outputs = model(train_tensor)\n",
    "    optimizer.zero_grad()\n",
    "    loss = criterion(outputs, train_tensor[:, -1, :])\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    if (epoch+1) % 150 == 0:\n",
    "        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n",
    "\n",
    "print(\"训练完成\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "5a20f7bf-d985-4f06-a785-31ac515bba07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "155726.35768261965"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.eval()\n",
    "test_outputs = model(test_tensor).detach().numpy()\n",
    "MSE(test,test_outputs)"
   ]
  }
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